Abstract

Developmental Genetic Programming (DGP) algorithms have been introduced where the search space for a problem is divided into genotypes and corresponding phenotypes that are connected by a mapping (or “genetic code”). Current implementations of this concept involve evolution of the mappings in addition to the traditional evolution of genotypes. We introduce the latest version of Probabilistic Adaptive Mapping DGP (PAM DGP), a robust and highly customizable algorithm that overcomes performance problems identified for the latest competing adaptive mapping algorithm. PAM DGP is then shown to outperform the competing algorithm on two non-trivial regression benchmarks.